SOURCE-LINKED INTELLIGENCE

PHYSICS INFORMED MACHINE LEARNING-BASED PREDICTION AND REVERSION OF IMPAIRED FASTING GLUCOSE MANAGEMENT

CORDIS · observation · Publication date unknown

PHYSICS INFORMED MACHINE LEARNING-BASED PREDICTION AND REVERSION OF IMPAIRED FASTING GLUCOSE MANAGEMENT The incidence of undiagnosed diabetes accounts for 36% European adults, while 541M adults worldwide have Impaired Glucose Tolerance (IGT), an important risk factor for further T2D development. Both IGT and/or Impaired Fasting Glucose (IFG) are intermediate glucose mishandling (i.e. intermediate conditions in the healthy-T2D transition) and are manifestations of the so-called prediabetes condition. Prediabetes itself is not an extensively studied condition compared to the overt T2D, but it is also a condition that can be reversed without the prescription usage to not proceed into T2D. The aim of our project is to develop a prototype tool for the real-time prediction of the prediabetic risk based on a series of patient-specific mathematical models (firstly developed during the FP7 MISSION-T2D project) th

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

5808740

unit

EUR